Abstract: An online system receives user data for users of the online system and assigns the users to one or more user cohorts based on the user data. The online system generates a prompt for content to be included in a landing page presented to each user cohort, in which the prompt includes a template for the landing page and information describing the user cohorts. The online system then provides the prompt to a generative artificial intelligence model to obtain an output and extracts, from the output, a set of content to be included in the landing page for each user cohort. The online system generates variants of the landing page for each user cohort based on the extracted set of content.

Abstract: An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

Abstract: An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

Abstract: An online concierge system receives information describing the progress of a picker servicing a batch of existing orders and predicts a first likelihood the picker will finish servicing the batch within a threshold amount of time based on the picker's progress and information describing the batch. If the first likelihood exceeds a threshold likelihood, the system accesses a machine learning model trained to predict a second likelihood the picker will accept a batch of new orders for servicing while servicing the batch of existing orders. The system applies the model to inputs including a set of attributes of the picker and the picker's progress to predict the second likelihood. The system matches batches of new orders with pickers based on the second likelihood and sends one or more requests to service one or more batches matched with the picker to a client device associated with the picker.

Abstract: An online concierge system receives information describing the progress of a picker servicing a batch of existing orders and a service request for an order. The system identifies picker attributes of the picker and order attributes of the order and each existing order of the set and accesses a machine learning model trained to predict a likelihood the picker will accept an add-on request to add the order to the batch of existing orders. To predict the likelihood, the system applies the model to the picker attributes, the progress of the picker, and the order attributes. The system determines a cost associated with sending the add-on request to the picker based on the likelihood and assigns the order to a set of orders based on the cost. The system sends the add-on request to the picker responsive to determining the order is assigned to the batch of existing orders.

Abstract: An online concierge system allows customers to place orders to be fulfilled by pickers. An order includes an amount of compensation a customer provides to a picker when the order is fulfilled. A customer may modify the amount of compensation provided to a picker, so some customers may initially specify a large amount of compensation to entice a picker to fulfill an order and then reduce the amount of compensation when the order is fulfilled. To prevent penalizing pickers who fulfilled an order without a problem, the online concierge system trains a model to determine a probability that a reduction in compensation to a picker was unrelated to a problem with order fulfillment. The online concierge system may perform one or more remedial actions for a picker based on the probability determined by the model.

Abstract: An online concierge system uses a machine-learned parking quality model to quantify the suitability of a particular parking location (e.g., a parking lot, or a street) for use when performing purchases at a retail location on behalf of customers. The parking quality model's output is determined according to input features related to parking at a candidate parking location, such as a current time, a current degree of demand for shoppers at the retail location, or a current average shopper wait time at the retail location before receiving an order. The online concierge system provides suggested alternate parking locations to a client device of the shopper, where they may be displayed, e.g., as part of an electronic map. Use of the suggested alternate parking locations helps to preserve parking availability in restricted areas such as retailer parking lots and to reduce traffic congestion in the area of the retailer.

Abstract: An online concierge system allows users to place orders for fulfillment by pickers. Orders have various attributes (e.g., dimensions, weight, contents, etc.), and the pickers may have corresponding characteristics affecting capability of fulfilling orders. To optimize allocation of orders to pickers for fulfillment, the online concierge system trains an order validation model that predicts a probability of a picker encountering a problem fulfilling an order based on characteristics of the picker and attributes of the order. The order validation model is trained from training examples based on previous orders and labels indicating whether a picker encountered a problem with fulfilling the order. The order validation model can then be used to predict deliverability of future orders or to specify limits on one or more attributes of orders for fulfillment.

Abstract: An online concierge system schedules pickers (shoppers) to fulfill orders from users. During periods of peak demand, the system increases compensation to shoppers to encourage more to participate, thereby reducing missed orders. The system determines an optimal multiplier to increase compensation based on predictive models of supply and demand and then applying an optimization algorithm to search different hyperparameters that affect how the models generate the multipliers. The system selects the optimal multipliers for different time periods and locations. The system may further present the multipliers being offered during future time periods and enable users to activate reminder alerts for select periods. The offers may be presented in a ranked list using a model trained to infer likelihoods of the user accepting participation and/or setting a reminder notification.

Abstract: Techniques for predicting a wait time for a shopper based on a location the shopper's client device are presented. A system identifies a shopper's current location and uses a machine learning model to predict a wait time until the shopper will receive one or more orders. The machine learning model is trained to use input features including a number of orders received during a current time period for fulfillment near the current location, a number of other shoppers available for fulfilling orders during the current time period near the current location, historical information about a presentation of a plurality of orders to a plurality of shoppers near the current location, and historical information about the shopper and the other nearby available shoppers. The system then sends the predicted wait time to the client device for presentation to the shopper.

Abstract: An online concierge system iteratively makes a batch of one or more orders available to an increasing number of shoppers to choose to fulfill. Each shopper may choose to accept or reject a batch for fulfillment. To improve batch acceptance and matching between batches and shoppers, the batches are scored with respect to expected resource costs, likelihood of acceptance by the shopper, and/or other quality metrics to iteratively offer the batch to an increasing number of shoppers (prioritizing the scoring factors) until a shopper accepts. The number of shoppers notified of the batch and the frequency that additional shoppers are selected may vary based on characteristics of the batch and likelihood the batch will be accepted by a shopper.

Abstract: The online concierge system generates task units based on orders and assigns batches of task units to pickers. The online concierge system generates task units based on received orders. The online concierge system generates permutations of these task units to generate candidate sets of task batches. The online concierge system scores each of these candidate sets, and selects a set of task batches to assign to pickers based on the scores. Additionally, to determine which task UI to display to the picker, the picker client device uses a UI state machine. The UI state machine is a state machine where each state corresponds to a task UI to display on the picker client device. The state transitions between the UI states of the UI state machine indicate which UI state to transition to from a current UI state based on the next task unit in the received task batch.

Abstract: An online concierge system receives orders from users that include items from one or more warehouses. The online concierge system identifies the orders to shoppers, who select one or more orders to fulfill. The online concierge system uses models to estimate orders likely to be received at different times and shoppers likely to be available to fulfill orders at different times. Responsive to greater than a threshold difference between estimated orders and estimated shoppers during a time interval, the online concierge system selects one or more incentives for shoppers to select orders during the time interval to entice shoppers to select orders during the time interval. An interface displayed to the shoppers by the online concierge system may present a map of warehouses and their estimated number of orders and allow shoppers to identify incentives offered for fulfilling orders at different warehouses during the time interval.

Abstract: This disclosure describes a transportation matching system that utilizes one or more balancer models to generate an electronic communication distribution strategy based on relative impacts of provider-specific and requester-specific levers over a target time horizon. The disclosed systems utilize the balancer models to generate predictive functions for providers and requesters to determine lever content to distribute (e.g., within electronic communications) to providers and/or requesters to efficiently and/or effectively produce acquisition and/or engagement for a target time horizon. Based on the predictive functions, the disclosed systems generate an electronic communication distribution strategy to provide (or cause to be provided) electronic communications to providers and requesters to efficiently and effectively increase or decrease acquisition and/or engagement.